Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data
Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artif...
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Veröffentlicht in: | Water (Basel) 2019-08, Vol.11 (8), p.1653 |
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description | Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms. |
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In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.</description><identifier>ISSN: 2073-4441</identifier><identifier>EISSN: 2073-4441</identifier><identifier>DOI: 10.3390/w11081653</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Datasets ; Evolutionary algorithms ; Floods ; Gauges ; Genetic algorithms ; Hydrologic data ; Hydrometeorology ; Neural networks ; Optical thickness ; Optimization ; Optimization algorithms ; Precipitation ; Rain gauges ; Rainfall ; Root-mean-square errors ; Satellites ; Sensitivity analysis ; Sensors ; Statistical methods ; Variables</subject><ispartof>Water (Basel), 2019-08, Vol.11 (8), p.1653</ispartof><rights>2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c292t-bd7046f04ac79962971fb64ed52e8237a97f1c90f5b94da2bcc2c628c5c6a4083</citedby><cites>FETCH-LOGICAL-c292t-bd7046f04ac79962971fb64ed52e8237a97f1c90f5b94da2bcc2c628c5c6a4083</cites><orcidid>0000-0003-0463-7386</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Salimi, Amir Hossein</creatorcontrib><creatorcontrib>Masoompour Samakosh, Jafar</creatorcontrib><creatorcontrib>Sharifi, Ehsan</creatorcontrib><creatorcontrib>Hassanvand, Mohammad Reza</creatorcontrib><creatorcontrib>Noori, Amir</creatorcontrib><creatorcontrib>von Rautenkranz, Hary</creatorcontrib><title>Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data</title><title>Water (Basel)</title><description>Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Datasets</subject><subject>Evolutionary algorithms</subject><subject>Floods</subject><subject>Gauges</subject><subject>Genetic algorithms</subject><subject>Hydrologic data</subject><subject>Hydrometeorology</subject><subject>Neural networks</subject><subject>Optical thickness</subject><subject>Optimization</subject><subject>Optimization algorithms</subject><subject>Precipitation</subject><subject>Rain gauges</subject><subject>Rainfall</subject><subject>Root-mean-square errors</subject><subject>Satellites</subject><subject>Sensitivity analysis</subject><subject>Sensors</subject><subject>Statistical methods</subject><subject>Variables</subject><issn>2073-4441</issn><issn>2073-4441</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNkE1LAzEURYMoWGoX_oOAKxej-ZyZLGurVahWUNdDJh-a2k7GJKXorze1Ir7NvYvDe7wDwClGF5QKdLnFGNW45PQADAiqaMEYw4f_-jEYxbhEeZioa44GYLXok1u7L6PhOCRnnXJyBR_MJvxE2vrwHosrGTNwb9Kb1xFaH-BTksnF5FTGpn7bxVxc9wq9hbPgtM74YzDK9W4H-g5OZZIn4MjKVTSj3xyCl5vr58ltMV_M7ibjeaGIIKlodYVYaRGTqhKiJKLCti2Z0ZyYmtBKispiJZDlrWBaklYpokpSK65KyVBNh-Bsv7cP_mNjYmqWfhO6fLIhnOffsyKWqfM9pYKPMRjb9MGtZfhsMGp2Pps_n_QbpY9oFw</recordid><startdate>20190801</startdate><enddate>20190801</enddate><creator>Salimi, Amir Hossein</creator><creator>Masoompour Samakosh, Jafar</creator><creator>Sharifi, Ehsan</creator><creator>Hassanvand, Mohammad Reza</creator><creator>Noori, Amir</creator><creator>von Rautenkranz, Hary</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0003-0463-7386</orcidid></search><sort><creationdate>20190801</creationdate><title>Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data</title><author>Salimi, Amir Hossein ; Masoompour Samakosh, Jafar ; Sharifi, Ehsan ; Hassanvand, Mohammad Reza ; Noori, Amir ; von Rautenkranz, Hary</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c292t-bd7046f04ac79962971fb64ed52e8237a97f1c90f5b94da2bcc2c628c5c6a4083</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Datasets</topic><topic>Evolutionary algorithms</topic><topic>Floods</topic><topic>Gauges</topic><topic>Genetic algorithms</topic><topic>Hydrologic data</topic><topic>Hydrometeorology</topic><topic>Neural networks</topic><topic>Optical thickness</topic><topic>Optimization</topic><topic>Optimization algorithms</topic><topic>Precipitation</topic><topic>Rain gauges</topic><topic>Rainfall</topic><topic>Root-mean-square errors</topic><topic>Satellites</topic><topic>Sensitivity analysis</topic><topic>Sensors</topic><topic>Statistical methods</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Salimi, Amir Hossein</creatorcontrib><creatorcontrib>Masoompour Samakosh, Jafar</creatorcontrib><creatorcontrib>Sharifi, Ehsan</creatorcontrib><creatorcontrib>Hassanvand, Mohammad Reza</creatorcontrib><creatorcontrib>Noori, Amir</creatorcontrib><creatorcontrib>von Rautenkranz, Hary</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Water (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Salimi, Amir Hossein</au><au>Masoompour Samakosh, Jafar</au><au>Sharifi, Ehsan</au><au>Hassanvand, Mohammad Reza</au><au>Noori, Amir</au><au>von Rautenkranz, Hary</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data</atitle><jtitle>Water (Basel)</jtitle><date>2019-08-01</date><risdate>2019</risdate><volume>11</volume><issue>8</issue><spage>1653</spage><pages>1653-</pages><issn>2073-4441</issn><eissn>2073-4441</eissn><abstract>Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical variables. The particle swarm optimization (PSO), imperialist competitive algorithm,(ICA), and genetic algorithm (GA) were utilized to improve the performance of ANN. Moreover, to examine the efficiency of the networks, the downscaled product was evaluated using 54 rain gauges at a daily timescale. In addition, sensitivity analysis was conducted to obtain the most and least influential input parameters. Among the optimized algorithms for network training used in this study, the performance of the ICA slightly outperformed other algorithms. The best-recorded performance for ICA was on 17 April 2015 with root mean square error (RMSE) = 5.26 mm, mean absolute error (MAE) = 6.06 mm, R2 = 0.67, bias = 0.07 mm. The results showed that the prediction of precipitation was more sensitive to cloud optical thickness (COT). Moreover, the accuracy of the final downscaled satellite precipitation was improved significantly through residual correction algorithms.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/w11081653</doi><orcidid>https://orcid.org/0000-0003-0463-7386</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Algorithms Datasets Evolutionary algorithms Floods Gauges Genetic algorithms Hydrologic data Hydrometeorology Neural networks Optical thickness Optimization Optimization algorithms Precipitation Rain gauges Rainfall Root-mean-square errors Satellites Sensitivity analysis Sensors Statistical methods Variables |
title | Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data |
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